Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations1519
Missing cells1755
Missing cells (%)6.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory951.9 KiB
Average record size in memory641.7 B

Variable types

Numeric10
Categorical6
Text3

Alerts

Purpose has constant value " For Sale" Constant
Bathrooms is highly overall correlated with Bedrooms and 3 other fieldsHigh correlation
Bedrooms is highly overall correlated with Bathrooms and 4 other fieldsHigh correlation
City is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Kitchens is highly overall correlated with Bathrooms and 1 other fieldsHigh correlation
Parking Spaces is highly overall correlated with Bedrooms and 1 other fieldsHigh correlation
Unnamed: 0 is highly overall correlated with City and 1 other fieldsHigh correlation
area is highly overall correlated with Bathrooms and 3 other fieldsHigh correlation
price is highly overall correlated with Bathrooms and 3 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property Type is highly overall correlated with provinceHigh correlation
province is highly overall correlated with City and 2 other fieldsHigh correlation
property Type is highly imbalanced (76.3%) Imbalance
City has 153 (10.1%) missing values Missing
Parking Spaces has 694 (45.7%) missing values Missing
Bedrooms has 112 (7.4%) missing values Missing
Bathrooms has 112 (7.4%) missing values Missing
Servant Quarters has 112 (7.4%) missing values Missing
Kitchens has 112 (7.4%) missing values Missing
Store Rooms has 112 (7.4%) missing values Missing
Purpose has 65 (4.3%) missing values Missing
area has 65 (4.3%) missing values Missing
price_per_sqft has 65 (4.3%) missing values Missing
property Type has 153 (10.1%) missing values Missing
Parking Spaces is highly skewed (γ1 = 23.22317222) Skewed
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
House Number has unique values Unique
Bedrooms has 47 (3.1%) zeros Zeros
Bathrooms has 54 (3.6%) zeros Zeros
Servant Quarters has 463 (30.5%) zeros Zeros
Kitchens has 129 (8.5%) zeros Zeros

Reproduction

Analysis started2025-02-27 17:46:07.441851
Analysis finished2025-02-27 17:46:31.072034
Duration23.63 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct1519
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean759
Minimum0
Maximum1518
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-02-27T17:46:31.228830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75.9
Q1379.5
median759
Q31138.5
95-th percentile1442.1
Maximum1518
Range1518
Interquartile range (IQR)759

Descriptive statistics

Standard deviation438.64184
Coefficient of variation (CV)0.57792074
Kurtosis-1.2
Mean759
Median Absolute Deviation (MAD)380
Skewness0
Sum1152921
Variance192406.67
MonotonicityStrictly increasing
2025-02-27T17:46:31.443878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
1009 1
 
0.1%
1018 1
 
0.1%
1017 1
 
0.1%
1016 1
 
0.1%
1015 1
 
0.1%
1014 1
 
0.1%
1013 1
 
0.1%
1012 1
 
0.1%
1011 1
 
0.1%
Other values (1509) 1509
99.3%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
ValueCountFrequency (%)
1518 1
0.1%
1517 1
0.1%
1516 1
0.1%
1515 1
0.1%
1514 1
0.1%
1513 1
0.1%
1512 1
0.1%
1511 1
0.1%
1510 1
0.1%
1509 1
0.1%

House Number
Real number (ℝ)

Unique 

Distinct1519
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51403235
Minimum25757819
Maximum52144273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-02-27T17:46:32.063334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25757819
5-th percentile48636648
Q151506872
median51984001
Q352076958
95-th percentile52126829
Maximum52144273
Range26386454
Interquartile range (IQR)570086

Descriptive statistics

Standard deviation1548566.8
Coefficient of variation (CV)0.030125863
Kurtosis64.4969
Mean51403235
Median Absolute Deviation (MAD)127727
Skewness-5.9801436
Sum7.8081513 × 1010
Variance2.3980592 × 1012
MonotonicityNot monotonic
2025-02-27T17:46:32.285680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48022756 1
 
0.1%
50962934 1
 
0.1%
52131690 1
 
0.1%
51886212 1
 
0.1%
52016360 1
 
0.1%
52008864 1
 
0.1%
52013728 1
 
0.1%
52024742 1
 
0.1%
52130214 1
 
0.1%
52130252 1
 
0.1%
Other values (1509) 1509
99.3%
ValueCountFrequency (%)
25757819 1
0.1%
37107033 1
0.1%
37264255 1
0.1%
40966074 1
0.1%
42475075 1
0.1%
42644685 1
0.1%
43542179 1
0.1%
43639626 1
0.1%
43804041 1
0.1%
43841758 1
0.1%
ValueCountFrequency (%)
52144273 1
0.1%
52143268 1
0.1%
52143266 1
0.1%
52142911 1
0.1%
52142446 1
0.1%
52140372 1
0.1%
52140252 1
0.1%
52140251 1
0.1%
52139999 1
0.1%
52139919 1
0.1%

City
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.2%
Missing153
Missing (%)10.1%
Memory size96.0 KiB
Lahore
582 
Rawalpindi
504 
Karachi
280 

Length

Max length10
Median length7
Mean length7.6808199
Min length6

Characters and Unicode

Total characters10492
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRawalpindi
2nd rowRawalpindi
3rd rowRawalpindi
4th rowRawalpindi
5th rowRawalpindi

Common Values

ValueCountFrequency (%)
Lahore 582
38.3%
Rawalpindi 504
33.2%
Karachi 280
18.4%
(Missing) 153
 
10.1%

Length

2025-02-27T17:46:32.485392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-27T17:46:32.618957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
lahore 582
42.6%
rawalpindi 504
36.9%
karachi 280
20.5%

Most occurring characters

ValueCountFrequency (%)
a 2150
20.5%
i 1288
12.3%
h 862
8.2%
r 862
8.2%
L 582
 
5.5%
o 582
 
5.5%
e 582
 
5.5%
R 504
 
4.8%
w 504
 
4.8%
l 504
 
4.8%
Other values (5) 2072
19.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9126
87.0%
Uppercase Letter 1366
 
13.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2150
23.6%
i 1288
14.1%
h 862
9.4%
r 862
9.4%
o 582
 
6.4%
e 582
 
6.4%
w 504
 
5.5%
l 504
 
5.5%
p 504
 
5.5%
n 504
 
5.5%
Other values (2) 784
 
8.6%
Uppercase Letter
ValueCountFrequency (%)
L 582
42.6%
R 504
36.9%
K 280
20.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 10492
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2150
20.5%
i 1288
12.3%
h 862
8.2%
r 862
8.2%
L 582
 
5.5%
o 582
 
5.5%
e 582
 
5.5%
R 504
 
4.8%
w 504
 
4.8%
l 504
 
4.8%
Other values (5) 2072
19.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2150
20.5%
i 1288
12.3%
h 862
8.2%
r 862
8.2%
L 582
 
5.5%
o 582
 
5.5%
e 582
 
5.5%
R 504
 
4.8%
w 504
 
4.8%
l 504
 
4.8%
Other values (5) 2072
19.7%

Parking Spaces
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct17
Distinct (%)2.1%
Missing694
Missing (%)45.7%
Infinite0
Infinite (%)0.0%
Mean3.3660606
Minimum0
Maximum400
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-02-27T17:46:32.764474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum400
Range400
Interquartile range (IQR)2

Descriptive statistics

Standard deviation15.007015
Coefficient of variation (CV)4.4583318
Kurtosis598.07999
Mean3.3660606
Median Absolute Deviation (MAD)1
Skewness23.223172
Sum2777
Variance225.2105
MonotonicityNot monotonic
2025-02-27T17:46:32.990630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 259
 
17.1%
2 234
 
15.4%
3 146
 
9.6%
4 135
 
8.9%
6 19
 
1.3%
5 14
 
0.9%
20 3
 
0.2%
8 3
 
0.2%
10 3
 
0.2%
100 2
 
0.1%
Other values (7) 7
 
0.5%
(Missing) 694
45.7%
ValueCountFrequency (%)
0 1
 
0.1%
1 259
17.1%
2 234
15.4%
3 146
9.6%
4 135
8.9%
5 14
 
0.9%
6 19
 
1.3%
8 3
 
0.2%
10 3
 
0.2%
15 1
 
0.1%
ValueCountFrequency (%)
400 1
 
0.1%
100 2
0.1%
60 1
 
0.1%
50 1
 
0.1%
25 1
 
0.1%
24 1
 
0.1%
20 3
0.2%
15 1
 
0.1%
10 3
0.2%
8 3
0.2%

Bedrooms
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct13
Distinct (%)0.9%
Missing112
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean4.0575693
Minimum0
Maximum13
Zeros47
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-02-27T17:46:33.139343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.701463
Coefficient of variation (CV)0.41933062
Kurtosis0.913183
Mean4.0575693
Median Absolute Deviation (MAD)1
Skewness-0.12554764
Sum5709
Variance2.8949764
MonotonicityNot monotonic
2025-02-27T17:46:33.297018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
5 503
33.1%
3 293
19.3%
4 200
 
13.2%
6 127
 
8.4%
2 103
 
6.8%
1 73
 
4.8%
0 47
 
3.1%
7 34
 
2.2%
8 16
 
1.1%
9 6
 
0.4%
Other values (3) 5
 
0.3%
(Missing) 112
 
7.4%
ValueCountFrequency (%)
0 47
 
3.1%
1 73
 
4.8%
2 103
 
6.8%
3 293
19.3%
4 200
 
13.2%
5 503
33.1%
6 127
 
8.4%
7 34
 
2.2%
8 16
 
1.1%
9 6
 
0.4%
ValueCountFrequency (%)
13 1
 
0.1%
11 1
 
0.1%
10 3
 
0.2%
9 6
 
0.4%
8 16
 
1.1%
7 34
 
2.2%
6 127
 
8.4%
5 503
33.1%
4 200
 
13.2%
3 293
19.3%

Bathrooms
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct13
Distinct (%)0.9%
Missing112
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean4.6204691
Minimum0
Maximum13
Zeros54
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-02-27T17:46:33.434884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q36
95-th percentile7
Maximum13
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0112292
Coefficient of variation (CV)0.43528681
Kurtosis0.25544019
Mean4.6204691
Median Absolute Deviation (MAD)1
Skewness-0.32458773
Sum6501
Variance4.045043
MonotonicityNot monotonic
2025-02-27T17:46:33.623258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
6 420
27.6%
4 237
15.6%
5 231
15.2%
3 141
 
9.3%
7 103
 
6.8%
2 90
 
5.9%
1 77
 
5.1%
0 54
 
3.6%
8 32
 
2.1%
10 13
 
0.9%
Other values (3) 9
 
0.6%
(Missing) 112
 
7.4%
ValueCountFrequency (%)
0 54
 
3.6%
1 77
 
5.1%
2 90
 
5.9%
3 141
 
9.3%
4 237
15.6%
5 231
15.2%
6 420
27.6%
7 103
 
6.8%
8 32
 
2.1%
9 5
 
0.3%
ValueCountFrequency (%)
13 1
 
0.1%
11 3
 
0.2%
10 13
 
0.9%
9 5
 
0.3%
8 32
 
2.1%
7 103
 
6.8%
6 420
27.6%
5 231
15.2%
4 237
15.6%
3 141
 
9.3%

Servant Quarters
Real number (ℝ)

Missing  Zeros 

Distinct7
Distinct (%)0.5%
Missing112
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean0.82942431
Minimum0
Maximum11
Zeros463
Zeros (%)30.5%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-02-27T17:46:33.751137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.74818977
Coefficient of variation (CV)0.90205913
Kurtosis25.167395
Mean0.82942431
Median Absolute Deviation (MAD)0
Skewness2.3497665
Sum1167
Variance0.55978793
MonotonicityNot monotonic
2025-02-27T17:46:33.881988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 749
49.3%
0 463
30.5%
2 179
 
11.8%
3 13
 
0.9%
6 1
 
0.1%
11 1
 
0.1%
4 1
 
0.1%
(Missing) 112
 
7.4%
ValueCountFrequency (%)
0 463
30.5%
1 749
49.3%
2 179
 
11.8%
3 13
 
0.9%
4 1
 
0.1%
6 1
 
0.1%
11 1
 
0.1%
ValueCountFrequency (%)
11 1
 
0.1%
6 1
 
0.1%
4 1
 
0.1%
3 13
 
0.9%
2 179
 
11.8%
1 749
49.3%
0 463
30.5%

Kitchens
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct7
Distinct (%)0.5%
Missing112
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean1.6062544
Minimum0
Maximum21
Zeros129
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-02-27T17:46:34.029234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91450496
Coefficient of variation (CV)0.56934003
Kurtosis143.00315
Mean1.6062544
Median Absolute Deviation (MAD)0
Skewness6.5513379
Sum2260
Variance0.83631932
MonotonicityNot monotonic
2025-02-27T17:46:34.176581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 780
51.3%
1 409
26.9%
0 129
 
8.5%
3 83
 
5.5%
4 4
 
0.3%
5 1
 
0.1%
21 1
 
0.1%
(Missing) 112
 
7.4%
ValueCountFrequency (%)
0 129
 
8.5%
1 409
26.9%
2 780
51.3%
3 83
 
5.5%
4 4
 
0.3%
5 1
 
0.1%
21 1
 
0.1%
ValueCountFrequency (%)
21 1
 
0.1%
5 1
 
0.1%
4 4
 
0.3%
3 83
 
5.5%
2 780
51.3%
1 409
26.9%
0 129
 
8.5%

Store Rooms
Categorical

Missing 

Distinct5
Distinct (%)0.4%
Missing112
Missing (%)7.4%
Memory size89.6 KiB
1.0
719 
0.0
500 
2.0
174 
3.0
 
9
4.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4221
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 719
47.3%
0.0 500
32.9%
2.0 174
 
11.5%
3.0 9
 
0.6%
4.0 5
 
0.3%
(Missing) 112
 
7.4%

Length

2025-02-27T17:46:34.332351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-27T17:46:34.460770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 719
51.1%
0.0 500
35.5%
2.0 174
 
12.4%
3.0 9
 
0.6%
4.0 5
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1907
45.2%
. 1407
33.3%
1 719
 
17.0%
2 174
 
4.1%
3 9
 
0.2%
4 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2814
66.7%
Other Punctuation 1407
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1907
67.8%
1 719
 
25.6%
2 174
 
6.2%
3 9
 
0.3%
4 5
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 1407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4221
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1907
45.2%
. 1407
33.3%
1 719
 
17.0%
2 174
 
4.1%
3 9
 
0.2%
4 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4221
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1907
45.2%
. 1407
33.3%
1 719
 
17.0%
2 174
 
4.1%
3 9
 
0.2%
4 5
 
0.1%

price
Real number (ℝ)

High correlation 

Distinct348
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9260829
Minimum0.26
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-02-27T17:46:34.641123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.26
5-th percentile0.739
Q11.8
median3.15
Q36
95-th percentile15
Maximum58
Range57.74
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation5.6474432
Coefficient of variation (CV)1.1464369
Kurtosis19.062253
Mean4.9260829
Median Absolute Deviation (MAD)1.6
Skewness3.6394889
Sum7482.72
Variance31.893615
MonotonicityNot monotonic
2025-02-27T17:46:34.844514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.65 29
 
1.9%
1.85 28
 
1.8%
2.25 28
 
1.8%
4.5 25
 
1.6%
1.9 22
 
1.4%
3.5 22
 
1.4%
2.2 21
 
1.4%
2.3 21
 
1.4%
1.7 20
 
1.3%
5.5 20
 
1.3%
Other values (338) 1283
84.5%
ValueCountFrequency (%)
0.26 1
 
0.1%
0.28 1
 
0.1%
0.29 1
 
0.1%
0.31 1
 
0.1%
0.32 1
 
0.1%
0.35 1
 
0.1%
0.36 4
0.3%
0.4 1
 
0.1%
0.42 2
0.1%
0.43 1
 
0.1%
ValueCountFrequency (%)
58 1
 
0.1%
48.5 1
 
0.1%
44.5 1
 
0.1%
42 3
0.2%
40 2
0.1%
39 1
 
0.1%
38 1
 
0.1%
36 2
0.1%
35 1
 
0.1%
33 1
 
0.1%

Purpose
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing65
Missing (%)4.3%
Memory size97.9 KiB
For Sale
1454 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters13086
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row For Sale
2nd row For Sale
3rd row For Sale
4th row For Sale
5th row For Sale

Common Values

ValueCountFrequency (%)
For Sale 1454
95.7%
(Missing) 65
 
4.3%

Length

2025-02-27T17:46:35.049587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-27T17:46:35.153002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
for 1454
50.0%
sale 1454
50.0%

Most occurring characters

ValueCountFrequency (%)
2908
22.2%
F 1454
11.1%
o 1454
11.1%
r 1454
11.1%
S 1454
11.1%
a 1454
11.1%
l 1454
11.1%
e 1454
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7270
55.6%
Space Separator 2908
 
22.2%
Uppercase Letter 2908
 
22.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1454
20.0%
r 1454
20.0%
a 1454
20.0%
l 1454
20.0%
e 1454
20.0%
Uppercase Letter
ValueCountFrequency (%)
F 1454
50.0%
S 1454
50.0%
Space Separator
ValueCountFrequency (%)
2908
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10178
77.8%
Common 2908
 
22.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 1454
14.3%
o 1454
14.3%
r 1454
14.3%
S 1454
14.3%
a 1454
14.3%
l 1454
14.3%
e 1454
14.3%
Common
ValueCountFrequency (%)
2908
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2908
22.2%
F 1454
11.1%
o 1454
11.1%
r 1454
11.1%
S 1454
11.1%
a 1454
11.1%
l 1454
11.1%
e 1454
11.1%
Distinct142
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size135.1 KiB
2025-02-27T17:46:35.424335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length63
Median length60
Mean length34.007242
Min length3

Characters and Unicode

Total characters51657
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)4.2%

Sample

1st rowBahria Town Rawalpindi, Rawalpindi, Punjab,
2nd rowBahria Town Rawalpindi, Rawalpindi, Punjab,
3rd rowBahria Town Rawalpindi, Rawalpindi, Punjab,
4th rowBahria Town Rawalpindi, Rawalpindi, Punjab,
5th rowBahria Town Rawalpindi, Rawalpindi, Punjab,
ValueCountFrequency (%)
punjab 1233
18.9%
rawalpindi 968
14.8%
lahore 670
10.3%
town 608
9.3%
bahria 556
8.5%
karachi 291
 
4.5%
defence 281
 
4.3%
dha 281
 
4.3%
sindh 237
 
3.6%
road 115
 
1.8%
Other values (170) 1288
19.7%
2025-02-27T17:46:35.898563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6499
 
12.6%
6478
 
12.5%
, 4386
 
8.5%
n 3757
 
7.3%
i 3680
 
7.1%
e 2026
 
3.9%
h 1989
 
3.9%
r 1955
 
3.8%
o 1759
 
3.4%
w 1660
 
3.2%
Other values (47) 17468
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33600
65.0%
Uppercase Letter 7124
 
13.8%
Space Separator 6478
 
12.5%
Other Punctuation 4386
 
8.5%
Decimal Number 69
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6499
19.3%
n 3757
11.2%
i 3680
11.0%
e 2026
 
6.0%
h 1989
 
5.9%
r 1955
 
5.8%
o 1759
 
5.2%
w 1660
 
4.9%
d 1516
 
4.5%
u 1480
 
4.4%
Other values (16) 7279
21.7%
Uppercase Letter
ValueCountFrequency (%)
P 1339
18.8%
R 1134
15.9%
L 689
9.7%
T 640
9.0%
D 579
8.1%
B 573
8.0%
A 461
 
6.5%
S 408
 
5.7%
H 406
 
5.7%
K 301
 
4.2%
Other values (14) 594
8.3%
Decimal Number
ValueCountFrequency (%)
1 31
44.9%
3 27
39.1%
4 9
 
13.0%
2 1
 
1.4%
9 1
 
1.4%
Space Separator
ValueCountFrequency (%)
6478
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4386
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40724
78.8%
Common 10933
 
21.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6499
16.0%
n 3757
 
9.2%
i 3680
 
9.0%
e 2026
 
5.0%
h 1989
 
4.9%
r 1955
 
4.8%
o 1759
 
4.3%
w 1660
 
4.1%
d 1516
 
3.7%
u 1480
 
3.6%
Other values (40) 14403
35.4%
Common
ValueCountFrequency (%)
6478
59.3%
, 4386
40.1%
1 31
 
0.3%
3 27
 
0.2%
4 9
 
0.1%
2 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51657
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6499
 
12.6%
6478
 
12.5%
, 4386
 
8.5%
n 3757
 
7.3%
i 3680
 
7.1%
e 2026
 
3.9%
h 1989
 
3.9%
r 1955
 
3.8%
o 1759
 
3.4%
w 1660
 
3.2%
Other values (47) 17468
33.8%

Age Possession
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size103.3 KiB
Relatively New
770 
Undefined
458 
New Property
125 
Under Construction
102 
Moderately Old
 
40

Length

Max length18
Median length14
Mean length12.564845
Min length9

Characters and Unicode

Total characters19086
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowRelatively New
3rd rowUndefined
4th rowRelatively New
5th rowNew Property

Common Values

ValueCountFrequency (%)
Relatively New 770
50.7%
Undefined 458
30.2%
New Property 125
 
8.2%
Under Construction 102
 
6.7%
Moderately Old 40
 
2.6%
Old Property 24
 
1.6%

Length

2025-02-27T17:46:36.034933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-27T17:46:36.177479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
new 895
34.7%
relatively 770
29.8%
undefined 458
17.8%
property 149
 
5.8%
under 102
 
4.0%
construction 102
 
4.0%
old 64
 
2.5%
moderately 40
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e 3682
19.3%
l 1644
 
8.6%
i 1330
 
7.0%
n 1222
 
6.4%
t 1163
 
6.1%
d 1122
 
5.9%
1061
 
5.6%
y 959
 
5.0%
N 895
 
4.7%
w 895
 
4.7%
Other values (15) 5113
26.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15445
80.9%
Uppercase Letter 2580
 
13.5%
Space Separator 1061
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3682
23.8%
l 1644
10.6%
i 1330
 
8.6%
n 1222
 
7.9%
t 1163
 
7.5%
d 1122
 
7.3%
y 959
 
6.2%
w 895
 
5.8%
a 810
 
5.2%
v 770
 
5.0%
Other values (7) 1848
12.0%
Uppercase Letter
ValueCountFrequency (%)
N 895
34.7%
R 770
29.8%
U 560
21.7%
P 149
 
5.8%
C 102
 
4.0%
O 64
 
2.5%
M 40
 
1.6%
Space Separator
ValueCountFrequency (%)
1061
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18025
94.4%
Common 1061
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3682
20.4%
l 1644
 
9.1%
i 1330
 
7.4%
n 1222
 
6.8%
t 1163
 
6.5%
d 1122
 
6.2%
y 959
 
5.3%
N 895
 
5.0%
w 895
 
5.0%
a 810
 
4.5%
Other values (14) 4303
23.9%
Common
ValueCountFrequency (%)
1061
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3682
19.3%
l 1644
 
8.6%
i 1330
 
7.0%
n 1222
 
6.4%
t 1163
 
6.1%
d 1122
 
5.9%
1061
 
5.6%
y 959
 
5.0%
N 895
 
4.7%
w 895
 
4.7%
Other values (15) 5113
26.8%

area
Real number (ℝ)

High correlation  Missing 

Distinct163
Distinct (%)11.2%
Missing65
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean3075.9924
Minimum279
Maximum108900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-02-27T17:46:36.373967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum279
5-th percentile817
Q11361
median2277
Q33600
95-th percentile5869.75
Maximum108900
Range108621
Interquartile range (IQR)2239

Descriptive statistics

Standard deviation4168.4051
Coefficient of variation (CV)1.3551415
Kurtosis309.81095
Mean3075.9924
Median Absolute Deviation (MAD)916
Skewness14.138177
Sum4472493
Variance17375601
MonotonicityNot monotonic
2025-02-27T17:46:36.636091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1361 289
19.0%
2722 253
16.7%
5445 216
14.2%
1906 72
 
4.7%
10890 35
 
2.3%
817 35
 
2.3%
1634 29
 
1.9%
1089 26
 
1.7%
4500 26
 
1.7%
2178 20
 
1.3%
Other values (153) 453
29.8%
(Missing) 65
 
4.3%
ValueCountFrequency (%)
279 1
 
0.1%
327 2
 
0.1%
354 1
 
0.1%
381 2
 
0.1%
436 2
 
0.1%
463 1
 
0.1%
490 4
 
0.3%
540 1
 
0.1%
544 11
0.7%
572 1
 
0.1%
ValueCountFrequency (%)
108900 1
 
0.1%
49005 1
 
0.1%
43560 2
 
0.1%
32670 1
 
0.1%
23958 1
 
0.1%
21780 2
 
0.1%
16335 2
 
0.1%
11979 1
 
0.1%
10890 35
2.3%
9801 1
 
0.1%

price_per_sqft
Real number (ℝ)

High correlation  Missing 

Distinct722
Distinct (%)49.7%
Missing65
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean15291.294
Minimum550.96
Maximum62222.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-02-27T17:46:36.911059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum550.96
5-th percentile7794.8475
Q111559.763
median13915.305
Q317251.115
95-th percentile27645.017
Maximum62222.22
Range61671.26
Interquartile range (IQR)5691.3525

Descriptive statistics

Standard deviation6833.6796
Coefficient of variation (CV)0.44690003
Kurtosis7.6221363
Mean15291.294
Median Absolute Deviation (MAD)2685.08
Skewness2.1305255
Sum22233541
Variance46699177
MonotonicityNot monotonic
2025-02-27T17:46:37.288187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13960.32 27
 
1.8%
13592.95 24
 
1.6%
12123.44 23
 
1.5%
16531.96 22
 
1.4%
13774.1 19
 
1.3%
12490.82 19
 
1.3%
12858.19 17
 
1.1%
13225.57 17
 
1.1%
14327.7 16
 
1.1%
11756.06 16
 
1.1%
Other values (712) 1254
82.6%
(Missing) 65
 
4.3%
ValueCountFrequency (%)
550.96 1
0.1%
587.7 1
0.1%
1056.01 1
0.1%
1224.36 1
0.1%
1322.31 1
0.1%
1530.46 1
0.1%
1606.98 1
0.1%
2203.86 1
0.1%
2410.47 1
0.1%
2712.16 1
0.1%
ValueCountFrequency (%)
62222.22 1
0.1%
55555.56 1
0.1%
53259.87 1
0.1%
53240.74 1
0.1%
52777.78 1
0.1%
50925.93 1
0.1%
49888.89 1
0.1%
47777.78 1
0.1%
46296.3 2
0.1%
45913.68 1
0.1%

colony
Text

Distinct304
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size107.4 KiB
2025-02-27T17:46:37.843977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length52
Median length41
Mean length15.312706
Min length3

Characters and Unicode

Total characters23260
Distinct characters66
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)10.4%

Sample

1st rowBahria Town Phase 8
2nd rowBahria Town Phase 8
3rd rowBahria Town Phase 4
4th rowBahria Town Phase 8
5th rowBahria Greens
ValueCountFrequency (%)
phase 592
 
13.6%
bahria 543
 
12.5%
town 538
 
12.3%
dha 261
 
6.0%
8 241
 
5.5%
housing 122
 
2.8%
city 118
 
2.7%
6 90
 
2.1%
society 82
 
1.9%
2 80
 
1.8%
Other values (286) 1690
38.8%
2025-02-27T17:46:38.636807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3437
14.8%
a 2742
 
11.8%
h 1431
 
6.2%
e 1382
 
5.9%
i 1348
 
5.8%
r 1196
 
5.1%
o 1097
 
4.7%
n 1092
 
4.7%
s 1027
 
4.4%
P 746
 
3.2%
Other values (56) 7762
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14758
63.4%
Uppercase Letter 4277
 
18.4%
Space Separator 3437
 
14.8%
Decimal Number 779
 
3.3%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2742
18.6%
h 1431
9.7%
e 1382
9.4%
i 1348
9.1%
r 1196
8.1%
o 1097
7.4%
n 1092
 
7.4%
s 1027
 
7.0%
w 626
 
4.2%
t 453
 
3.1%
Other values (16) 2364
16.0%
Uppercase Letter
ValueCountFrequency (%)
P 746
17.4%
B 599
14.0%
T 557
13.0%
A 499
11.7%
H 423
9.9%
D 298
 
7.0%
C 243
 
5.7%
S 238
 
5.6%
G 156
 
3.6%
R 102
 
2.4%
Other values (16) 416
9.7%
Decimal Number
ValueCountFrequency (%)
8 242
31.1%
1 91
 
11.7%
6 90
 
11.6%
2 81
 
10.4%
7 74
 
9.5%
3 63
 
8.1%
4 44
 
5.6%
5 44
 
5.6%
9 35
 
4.5%
0 15
 
1.9%
Space Separator
ValueCountFrequency (%)
3437
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Other Punctuation
ValueCountFrequency (%)
& 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19035
81.8%
Common 4225
 
18.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2742
14.4%
h 1431
 
7.5%
e 1382
 
7.3%
i 1348
 
7.1%
r 1196
 
6.3%
o 1097
 
5.8%
n 1092
 
5.7%
s 1027
 
5.4%
P 746
 
3.9%
w 626
 
3.3%
Other values (42) 6348
33.3%
Common
ValueCountFrequency (%)
3437
81.3%
8 242
 
5.7%
1 91
 
2.2%
6 90
 
2.1%
2 81
 
1.9%
7 74
 
1.8%
3 63
 
1.5%
4 44
 
1.0%
5 44
 
1.0%
9 35
 
0.8%
Other values (4) 24
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3437
14.8%
a 2742
 
11.8%
h 1431
 
6.2%
e 1382
 
5.9%
i 1348
 
5.8%
r 1196
 
5.1%
o 1097
 
4.7%
n 1092
 
4.7%
s 1027
 
4.4%
P 746
 
3.2%
Other values (56) 7762
33.4%

province
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size93.3 KiB
Punjab
1238 
Sindh
281 

Length

Max length6
Median length6
Mean length5.8150099
Min length5

Characters and Unicode

Total characters8833
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPunjab
2nd rowPunjab
3rd rowPunjab
4th rowPunjab
5th rowPunjab

Common Values

ValueCountFrequency (%)
Punjab 1238
81.5%
Sindh 281
 
18.5%

Length

2025-02-27T17:46:38.851597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-27T17:46:38.971753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
punjab 1238
81.5%
sindh 281
 
18.5%

Most occurring characters

ValueCountFrequency (%)
n 1519
17.2%
P 1238
14.0%
u 1238
14.0%
j 1238
14.0%
a 1238
14.0%
b 1238
14.0%
S 281
 
3.2%
i 281
 
3.2%
d 281
 
3.2%
h 281
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7314
82.8%
Uppercase Letter 1519
 
17.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1519
20.8%
u 1238
16.9%
j 1238
16.9%
a 1238
16.9%
b 1238
16.9%
i 281
 
3.8%
d 281
 
3.8%
h 281
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
P 1238
81.5%
S 281
 
18.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 8833
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1519
17.2%
P 1238
14.0%
u 1238
14.0%
j 1238
14.0%
a 1238
14.0%
b 1238
14.0%
S 281
 
3.2%
i 281
 
3.2%
d 281
 
3.2%
h 281
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1519
17.2%
P 1238
14.0%
u 1238
14.0%
j 1238
14.0%
a 1238
14.0%
b 1238
14.0%
S 281
 
3.2%
i 281
 
3.2%
d 281
 
3.2%
h 281
 
3.2%
Distinct139
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size107.2 KiB
2025-02-27T17:46:39.380415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length45
Median length44
Mean length15.208032
Min length3

Characters and Unicode

Total characters23101
Distinct characters56
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)4.2%

Sample

1st rowBahria Town Rawalpindi
2nd rowBahria Town Rawalpindi
3rd rowBahria Town Rawalpindi
4th rowBahria Town Rawalpindi
5th rowBahria Town Rawalpindi
ValueCountFrequency (%)
town 608
16.9%
bahria 556
15.4%
rawalpindi 408
11.3%
defence 281
 
7.8%
dha 281
 
7.8%
road 115
 
3.2%
housing 115
 
3.2%
city 76
 
2.1%
society 76
 
2.1%
park 74
 
2.1%
Other values (169) 1014
28.1%
2025-02-27T17:46:39.935397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3015
 
13.1%
2092
 
9.1%
i 2086
 
9.0%
n 1735
 
7.5%
e 1361
 
5.9%
w 1100
 
4.8%
o 1094
 
4.7%
r 1053
 
4.6%
h 850
 
3.7%
d 719
 
3.1%
Other values (46) 7996
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16740
72.5%
Uppercase Letter 4200
 
18.2%
Space Separator 2092
 
9.1%
Decimal Number 69
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3015
18.0%
i 2086
12.5%
n 1735
10.4%
e 1361
8.1%
w 1100
 
6.6%
o 1094
 
6.5%
r 1053
 
6.3%
h 850
 
5.1%
d 719
 
4.3%
l 700
 
4.2%
Other values (16) 3027
18.1%
Uppercase Letter
ValueCountFrequency (%)
T 640
15.2%
D 579
13.8%
R 574
13.7%
B 573
13.6%
A 461
11.0%
H 406
9.7%
C 173
 
4.1%
S 171
 
4.1%
G 141
 
3.4%
P 114
 
2.7%
Other values (14) 368
8.8%
Decimal Number
ValueCountFrequency (%)
1 31
44.9%
3 27
39.1%
4 9
 
13.0%
9 1
 
1.4%
2 1
 
1.4%
Space Separator
ValueCountFrequency (%)
2092
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20940
90.6%
Common 2161
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3015
14.4%
i 2086
 
10.0%
n 1735
 
8.3%
e 1361
 
6.5%
w 1100
 
5.3%
o 1094
 
5.2%
r 1053
 
5.0%
h 850
 
4.1%
d 719
 
3.4%
l 700
 
3.3%
Other values (40) 7227
34.5%
Common
ValueCountFrequency (%)
2092
96.8%
1 31
 
1.4%
3 27
 
1.2%
4 9
 
0.4%
9 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3015
 
13.1%
2092
 
9.1%
i 2086
 
9.0%
n 1735
 
7.5%
e 1361
 
5.9%
w 1100
 
4.8%
o 1094
 
4.7%
r 1053
 
4.6%
h 850
 
3.7%
d 719
 
3.1%
Other values (46) 7996
34.6%

property Type
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.4%
Missing153
Missing (%)10.1%
Memory size93.6 KiB
Houses
1228 
Flats
 
118
Upper
 
13
Lower
 
6
Penthouse
 
1

Length

Max length9
Median length6
Mean length5.9019034
Min length5

Characters and Unicode

Total characters8062
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowHouses
2nd rowHouses
3rd rowHouses
4th rowHouses
5th rowHouses

Common Values

ValueCountFrequency (%)
Houses 1228
80.8%
Flats 118
 
7.8%
Upper 13
 
0.9%
Lower 6
 
0.4%
Penthouse 1
 
0.1%
(Missing) 153
 
10.1%

Length

2025-02-27T17:46:40.087906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-27T17:46:40.230470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
houses 1228
89.9%
flats 118
 
8.6%
upper 13
 
1.0%
lower 6
 
0.4%
penthouse 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
s 2575
31.9%
e 1249
15.5%
o 1235
15.3%
u 1229
15.2%
H 1228
15.2%
t 119
 
1.5%
l 118
 
1.5%
a 118
 
1.5%
F 118
 
1.5%
p 26
 
0.3%
Other values (7) 47
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6696
83.1%
Uppercase Letter 1366
 
16.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 2575
38.5%
e 1249
18.7%
o 1235
18.4%
u 1229
18.4%
t 119
 
1.8%
l 118
 
1.8%
a 118
 
1.8%
p 26
 
0.4%
r 19
 
0.3%
w 6
 
0.1%
Other values (2) 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
H 1228
89.9%
F 118
 
8.6%
U 13
 
1.0%
L 6
 
0.4%
P 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8062
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 2575
31.9%
e 1249
15.5%
o 1235
15.3%
u 1229
15.2%
H 1228
15.2%
t 119
 
1.5%
l 118
 
1.5%
a 118
 
1.5%
F 118
 
1.5%
p 26
 
0.3%
Other values (7) 47
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 2575
31.9%
e 1249
15.5%
o 1235
15.3%
u 1229
15.2%
H 1228
15.2%
t 119
 
1.5%
l 118
 
1.5%
a 118
 
1.5%
F 118
 
1.5%
p 26
 
0.3%
Other values (7) 47
 
0.6%

Interactions

2025-02-27T17:46:28.170693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-27T17:46:14.175370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:16.344313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:18.129467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:19.863109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:21.533998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:23.414107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:26.290303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:28.360078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:09.639811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:12.187981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-27T17:46:21.701421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:23.731605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-27T17:46:28.542677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-27T17:46:12.418699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-27T17:46:16.711154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-27T17:46:26.899372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-27T17:46:17.091213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-27T17:46:29.127925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:10.645987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-27T17:46:21.047407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:22.936804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:25.357597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:27.576771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:29.638259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:11.380356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:13.801307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-27T17:46:19.516726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-27T17:46:29.809443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:11.620471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:13.999373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:16.148942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:17.958296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:19.685153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:21.359714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:23.257871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:25.980246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T17:46:27.945164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-27T17:46:40.382999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Age PossessionBathroomsBedroomsCityHouse NumberKitchensParking SpacesServant QuartersStore RoomsUnnamed: 0areapriceprice_per_sqftproperty Typeprovince
Age Possession1.0000.1250.1070.1100.0000.0490.0150.0520.1660.0900.0430.0340.0610.0720.138
Bathrooms0.1251.0000.8960.271-0.0800.6130.4840.4020.288-0.1490.5760.5490.1990.2600.302
Bedrooms0.1070.8961.0000.224-0.0810.5860.5010.3900.278-0.1080.5910.5720.2170.2180.207
City0.1100.2710.2241.0000.0650.0670.0330.1780.1000.9480.0030.1160.2460.4641.000
House Number0.000-0.080-0.0810.0651.000-0.0340.002-0.1240.0000.205-0.040-0.0250.0570.0000.000
Kitchens0.0490.6130.5860.067-0.0341.0000.3350.4290.236-0.1490.2990.2730.1270.0210.045
Parking Spaces0.0150.4840.5010.0330.0020.3351.0000.3780.000-0.0420.5680.4960.0300.0000.056
Servant Quarters0.0520.4020.3900.178-0.1240.4290.3781.0000.232-0.0310.3800.3430.0730.0000.000
Store Rooms0.1660.2880.2780.1000.0000.2360.0000.2321.0000.0710.0000.1330.0720.0000.000
Unnamed: 00.090-0.149-0.1080.9480.205-0.149-0.042-0.0310.0711.000-0.0420.0710.1310.3080.954
area0.0430.5760.5910.003-0.0400.2990.5680.3800.000-0.0421.0000.8980.2500.0000.000
price0.0340.5490.5720.116-0.0250.2730.4960.3430.1330.0710.8981.0000.6070.0890.138
price_per_sqft0.0610.1990.2170.2460.0570.1270.0300.0730.0720.1310.2500.6071.0000.0910.310
property Type0.0720.2600.2180.4640.0000.0210.0000.0000.0000.3080.0000.0890.0911.0000.658
province0.1380.3020.2071.0000.0000.0450.0560.0000.0000.9540.0000.1380.3100.6581.000

Missing values

2025-02-27T17:46:30.135233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-27T17:46:30.428471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-27T17:46:30.815240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0House NumberCityParking SpacesBedroomsBathroomsServant QuartersKitchensStore RoomspricePurposeLocationAge Possessionareaprice_per_sqftcolonyprovincesocietyproperty Type
0048022756RawalpindiNaN5.06.01.02.00.07.50For SaleBahria Town Rawalpindi, Rawalpindi, Punjab,Relatively New5445.013774.10Bahria Town Phase 8PunjabBahria Town RawalpindiHouses
1151560745RawalpindiNaN5.06.01.02.00.03.75For SaleBahria Town Rawalpindi, Rawalpindi, Punjab,Relatively New2968.012634.77Bahria Town Phase 8PunjabBahria Town RawalpindiHouses
2251815448RawalpindiNaN5.06.01.02.00.04.25For SaleBahria Town Rawalpindi, Rawalpindi, Punjab,Undefined2722.015613.52Bahria Town Phase 4PunjabBahria Town RawalpindiHouses
3352061409Rawalpindi1.03.03.00.01.00.01.50For SaleBahria Town Rawalpindi, Rawalpindi, Punjab,Relatively New1361.011021.31Bahria Town Phase 8PunjabBahria Town RawalpindiHouses
4452010487Rawalpindi3.06.06.01.02.01.04.75For SaleBahria Town Rawalpindi, Rawalpindi, Punjab,New Property3812.012460.65Bahria GreensPunjabBahria Town RawalpindiHouses
5542644685NaN2.03.03.01.01.01.02.25For SaleAskari 14, Rawalpindi, Punjab,Moderately Old2722.08265.98Askari 14PunjabAskari 14NaN
6652099738NaN3.02.02.00.01.01.01.15For SaleBahria Town Rawalpindi, Rawalpindi, Punjab,Relatively New1171.09820.67Bahria TownPunjabBahria Town RawalpindiNaN
7751956291Rawalpindi1.04.05.00.00.00.01.75For SaleDefence Road, Rawalpindi, Punjab,Undefined1361.012858.19Defence RoadPunjabDefence RoadHouses
8852094137Rawalpindi1.04.04.00.02.00.01.55For SaleAdiala Road, Rawalpindi, Punjab,Under Construction1361.011388.68Snober CityPunjabAdiala RoadHouses
9952094145RawalpindiNaN3.04.00.01.00.01.35For SaleAdiala Road, Rawalpindi, Punjab,Under Construction1361.09919.18Adiala RoadPunjabAdiala RoadHouses
Unnamed: 0House NumberCityParking SpacesBedroomsBathroomsServant QuartersKitchensStore RoomspricePurposeLocationAge Possessionareaprice_per_sqftcolonyprovincesocietyproperty Type
1509150951461050KarachiNaN0.00.00.00.00.06.10For SaleDHA Defence, Karachi, Sindh,Undefined1998.030530.53Emaar Coral TowersSindhDHA DefenceFlats
1510151051525734Karachi1.03.03.01.01.02.02.29For SaleGulistanRelatively New1701.013462.67Kings PresidencySindhGulistanFlats
1511151148409814Karachi1.05.05.01.01.00.010.50For SaleMalir, Karachi, Sindh,New Property4500.023333.33Falcon Complex New MalirSindhMalirHouses
1512151249596308KarachiNaNNaNNaNNaNNaNNaN2.25For SaleBin Qasim Town, Karachi, Sindh,Relatively New1197.018796.99Model ColonySindhBin Qasim TownHouses
1513151352076865KarachiNaNNaNNaNNaNNaNNaN4.30For SaleClifton, Karachi, Sindh,Undefined2502.017186.25CliftonSindhCliftonFlats
1514151451979270KarachiNaNNaNNaNNaNNaNNaN2.45For SaleScheme 33, Karachi, Sindh,Undefined1080.022685.19Sector 31SindhScheme 33Houses
1515151552011503KarachiNaNNaNNaNNaNNaNNaN20.00For SaleDHA Defence, Karachi, Sindh,Undefined6003.033316.67Zamzama Commercial AreaSindhDHA DefencePenthouse
1516151651140390Karachi1.02.02.00.01.00.02.20For SaleJamshed Town, Karachi, Sindh,New Property900.024444.44PECHS Block 2SindhJamshed TownUpper
1517151750119841Karachi1.03.03.00.01.01.03.50For SaleJamshed Town, Karachi, Sindh,Relatively New1800.019444.44PECHS Block 2SindhJamshed TownLower
1518151849977228Karachi1.04.04.01.01.01.04.25For SaleJamshed Town, Karachi, Sindh,New Property2250.018888.89PECHS Block 2SindhJamshed TownUpper